%global __brp_check_rpaths %{nil} %global __requires_exclude ^libmpi %global packname GeoModels %global packver 2.0.2 %global rlibdir /usr/local/lib/R/library Name: R-CRAN-%{packname} Version: 2.0.2 Release: 1%{?dist}%{?buildtag} Summary: Procedures for Gaussian and Non Gaussian Geostatistical (Large) Data Analysis License: GPL (>= 3) URL: https://cran.r-project.org/package=%{packname} Source0: %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz BuildRequires: R-devel >= 4.1.0 Requires: R-core >= 4.1.0 BuildRequires: R-CRAN-fields BuildRequires: R-CRAN-mapproj BuildRequires: R-CRAN-shape BuildRequires: R-CRAN-codetools BuildRequires: R-methods BuildRequires: R-CRAN-spam BuildRequires: R-CRAN-scatterplot3d BuildRequires: R-CRAN-dotCall64 BuildRequires: R-CRAN-FastGP BuildRequires: R-CRAN-plotrix BuildRequires: R-CRAN-pracma BuildRequires: R-CRAN-pbivnorm BuildRequires: R-CRAN-zipfR BuildRequires: R-CRAN-sn BuildRequires: R-CRAN-sp BuildRequires: R-CRAN-lamW BuildRequires: R-CRAN-nabor BuildRequires: R-CRAN-hypergeo BuildRequires: R-CRAN-VGAM BuildRequires: R-CRAN-data.table BuildRequires: R-CRAN-foreach BuildRequires: R-CRAN-future BuildRequires: R-CRAN-doFuture BuildRequires: R-CRAN-progressr Requires: R-CRAN-fields Requires: R-CRAN-mapproj Requires: R-CRAN-shape Requires: R-CRAN-codetools Requires: R-methods Requires: R-CRAN-spam Requires: R-CRAN-scatterplot3d Requires: R-CRAN-dotCall64 Requires: R-CRAN-FastGP Requires: R-CRAN-plotrix Requires: R-CRAN-pracma Requires: R-CRAN-pbivnorm Requires: R-CRAN-zipfR Requires: R-CRAN-sn Requires: R-CRAN-sp Requires: R-CRAN-lamW Requires: R-CRAN-nabor Requires: R-CRAN-hypergeo Requires: R-CRAN-VGAM Requires: R-CRAN-data.table Requires: R-CRAN-foreach Requires: R-CRAN-future Requires: R-CRAN-doFuture Requires: R-CRAN-progressr %description Functions for Gaussian and Non Gaussian (bivariate) spatial and spatio-temporal data analysis are provided for a) (fast) simulation of random fields, b) inference for random fields using standard likelihood and a likelihood approximation method called weighted composite likelihood based on pairs and b) prediction using (local) best linear unbiased prediction. Weighted composite likelihood can be very efficient for estimating massive datasets. Both regression and spatial (temporal) dependence analysis can be jointly performed. Flexible covariance models for spatial and spatial-temporal data on Euclidean domains and spheres are provided. There are also many useful functions for plotting and performing diagnostic analysis. Different non Gaussian random fields can be considered in the analysis. Among them, random fields with marginal distributions such as Skew-Gaussian, Student-t, Tukey-h, Sin-Arcsin, Two-piece, Weibull, Gamma, Log-Gaussian, Binomial, Negative Binomial and Poisson. See the URL for the papers associated with this package, as for instance, Bevilacqua and Gaetan (2015) , Bevilacqua et al. (2016) , Vallejos et al. (2020) , Bevilacqua et. al (2020) , Bevilacqua et. al (2021) , Bevilacqua et al. (2022) , Morales-Navarrete et al. (2023) , and a large class of examples and tutorials. %prep %setup -q -c -n %{packname} # fix end of executable files find -type f -executable -exec grep -Iq . {} \; -exec sed -i -e '$a\' {} \; # prevent binary stripping [ -d %{packname}/src ] && find %{packname}/src -type f -exec \ sed -i 's@/usr/bin/strip@/usr/bin/true@g' {} \; || true [ -d %{packname}/src ] && find %{packname}/src/Make* -type f -exec \ sed -i 's@-g0@@g' {} \; || true # don't allow local prefix in executable scripts find -type f -executable -exec sed -Ei 's@#!( )*/usr/local/bin@#!/usr/bin@g' {} \; %build %install mkdir -p %{buildroot}%{rlibdir} %{_bindir}/R CMD INSTALL -l %{buildroot}%{rlibdir} %{packname} test -d %{packname}/src && (cd %{packname}/src; rm -f *.o *.so) rm -f %{buildroot}%{rlibdir}/R.css # remove buildroot from installed files find %{buildroot}%{rlibdir} -type f -exec sed -i "s@%{buildroot}@@g" {} \; %files %{rlibdir}/%{packname}